Publication Type
Journal Article
Version
publishedVersion
Publication Date
3-2022
Abstract
Anomaly detection, a.k.a. outlier detection or novelty detection, has been a lasting yet active research area in various research communities for several decades. There are still some unique problem complexities and challenges that require advanced approaches. In recent years, deep learning enabled anomaly detection, i.e., deep anomaly detection, has emerged as a critical direction. This article surveys the research of deep anomaly detection with a comprehensive taxonomy, covering advancements in 3 high-level categories and 11 fine-grained categories of the methods. We review their key intuitions, objective functions, underlying assumptions, advantages, and disadvantages and discuss how they address the aforementioned challenges. We further discuss a set of possible future opportunities and new perspectives on addressing the challenges.
Keywords
Anomaly detection, deep learning, outlier detection, novelty detection, one-class classification
Discipline
Artificial Intelligence and Robotics | Databases and Information Systems
Research Areas
Intelligent Systems and Optimization
Publication
ACM Computing Surveys
Volume
54
Issue
2
First Page
1
Last Page
38
ISSN
0360-0300
Identifier
10.1145/3439950
Publisher
ACM
Citation
PANG, Guansong; SHEN, Chunhua; CAO, Longbing; and Van Den HENGEL, Anton.
Deep learning for anomaly detection: A review. (2022). ACM Computing Surveys. 54, (2), 1-38.
Available at: https://ink.library.smu.edu.sg/sis_research/7016
Copyright Owner and License
Publisher
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/3439950